The research objective of this Faculty Early Career Development (CAREER) project is to investigate new optimization models and algorithms that enable decision-makers to manage risk when making complex decisions in the face of uncertainty. Specific research activities include: explore new decomposition-based methods for stochastic programs having constraints limiting the probability of a bad outcome; study new stochastic dominance constrained models that allow decision-makers to set a minimally acceptable risk profile in an adjustable manner; and prove the usability and impact of these models by using them to solve problems in application areas such as supply chain management, workforce staffing, finance, and medical treatment planning. The overarching educational objective of this CAREER project is to increase the population of individuals who use quantitative tools to make decisions in the face of uncertainty. Specific educational activities include: work with a campus precollege outreach organization to incorporate optimization-related topics into their programs; develop and teach a decision-making under uncertainty module for an introductory engineering course; and design and implement a graduate course on optimization under uncertainty.
This project will lead to a new set of computationally practical tools that decision-makers can use to find plans that balance risk and efficiency. For example, these tools could be used by businesses to meet customer service targets at lower cost, by government agencies to choose cost-effective disease control policies, or by physicians to design radiation treatment plans. The educational aspect of the proposal will facilitate adoption of the methods by giving graduate students the skills needed to apply them, and by exposing a wide range of undergraduate students to the use of optimization. Precollege educational activities will promote diversity in the mathematical sciences by reaching an economically diverse range of precollege students across Wisconsin.
